technical investigations rarely lead to practical, interactive instruments that favour accountability and, conversely, critical studies of accountability are still lacking tools for a technical analysis of the AI black box.

So implies also to bridge gaps between

• academic disciplines (critical AI studies; explainable AI/ML)
• societal sectors (research, activism, art)

Accountability through interpretability

• developing—and demonstrating—a “generalized sociotechnical methodology”, commensurate to the effects AI brings on a societal level
• dogfooding the creation of software—a platform of tools enabling an integrated, “full-stack” approach, also for e.g. researchers new to AI analysis

Project dimensions

Two pillars/output orientations

• Sociotechnical case studies
• Forensics toolkit

Project structure

Project structure

Toolkit components

Work plan

gantt
    dateFormat YY-MM
    axisFormat %Y W%V
    todayMarker off

    section Project coordination
   %% Application submission  :milestone, submitted, 2021-11-24, 1d
    Project Start :milestone, startv, 22-05, 1d
    Hiring Soc. Phds : hiresocs, after startv , 60d
    Hiring Code Phds : hirecode, after startv , 121d

    section Methodology design
    Inaugural workshop :crit, ws1 , after hiresocs , 1d
    Sociotech. case study research design :scsrd, after ws1 , 183d
    Second workshop :crit, ws2 , after hirecode , 61d

    section Implement method—Data 
    Dataset toolkit : datatk , after hirecode , 244d
    Sociotech. case study data coll. :scsdg, after scsrd , 61d
    Sociotech. case study data & model forensics :crit , scsdatamodforensics , after scsdg , 61d
    
    section Implement/co-develop Model analysis
    Model forensics dev. : modeltk , after scsdg , 427d
    Sociotech. case study model forensics, : scsmodfr , after scsdg , 122d
    Third workshop :crit, ws3 , after scsmodfr , 61d

    section Adversarial dev.
    Application forensics dev. :apptk , after scsmodfr , 397d
    Sociotech. case study model/appl. forensics : scsmodapplforensics , after ws3 , 61d
    Fourth workshop :crit, ws4 , after scsmodapplforensics , 61d

    section Sociotechnical Case-studies scholarly side
    Sociotech. case studies read-write : scsrw , after hiresocs , until integr

    section Integration
    Integration of toolkit and case studies :integr , after scsmodapplforensics , 365d

The factors

Transformers’ gauntlet

The foundation model/transformer/LLM era redrew the map of AI on a societal level—and thereby also directly bore on the normative impetus of the project as such—

• Architectural homogenisation: transformer/attention paradigm
• Generative AI’s sudden affinity with language and visual culture
• Extreme polarisations:
  • Discursively: ‘Research’ AI discourse; control/regulatory debates
  • Access, in the proprietary/API-gated phase
  • Scale

Toolkit proposal vs. transformer paradigm

Across every dimension: scale and access

Morale

• Haphazard momentum at beginning (staffing, bureaucratically, planning-overhead)—

• …coincided with discovery that a decent part of the common project was being rendered obsolete, assumptions had to be rethought…

• and fed into a minor crisis of project coherence, including renaming discussions

Explainability beyond explainability

• How can explainability/interpretability/understandability be addressed through design- and interaction-related concepts?
• Feminist epistemologies highlighting pluriversal perspectives and the situatedness of knowledge (Klumbyte, Piehl, and Draude 2023b, 2023a)
• Mapping the ‘design diagrams’—e.g. the organisation of the production processes creating AI models
• Response-ability
• Understand explainability and understandability not merely as properties of an AI system, but as emerging in interaction—pointing to roles of
  • interaction modalities (e.g. embodiment),
  • metaphors (e.g. focus on transparency as a pre-requisite)
  • sociocultural factors (e.g. uneven distribution of power and resources)

Explainability beyond explainability

Embodiment and tangibility

Models of?

Pedagogies of—with—after AI?

• Cambridge – Dare – Generative AI UnToolkit
• Karlsruhe – Caffoni – A Pedagogy of Machines
• Kassel — Klumbytė/Draude – useXAI: The Use and Explainability Needs of Generative AI Tools among Students (led by Participatory IT Design group in Kassel, collaboration with Faculty of Social Sciences and Human Sciences) –

Medical AI

G. G.

Mechanistic interpretability

Rediscovering the ‘soul of the project’

or: “the importance of meeting in-person”

the friction between the social and the technical

sensitivity to how ways of thinking about social forms hide their way into technical forms and have a sort of Nachleben

• affinity/proximity to the notion of boundary object(Star 1989, 2010; Star and Ruhleder 1996)

• understanding of these systems as epistemic machines

• Glossary: Interpreting machine learning: a sociotechnical glossary

Output/s

Output/s

After the ‘Full stack interface’

• SAE dashboard/s
• Critical-pedagogical interfaces
• Embodiment
• Latent space of glossary